User profiles are used with a wide variety of applications and have varying levels of descriptivity and complexity. At its most basic level the user profile is a simple form with predefined fields, for example string entries giving values for given fields. However, the categorization of the fields together with the use of the data contained therein varies widely from application to application.
One common use for a user profile is found in the field of on-line searching where the profile is used to filter results to yield only relevant information to a certain user as search results.
This is discussed for example in the paper “Data Personalization; a Taxonomy of User Profiles Knowledge and a Profile Management Tool” by Mokrane Bouzeghoub and Dimitre Kostadinov (of the Laboratoire PRiSM, Université de Versaillessee), which describes data personalization to facilitate the expression of the need of a particular user and to enable him to obtain relevant information when he accesses an information system. That is, the user profile contains information about the user's preferences which is then used to filter search results when he searches for goods and services.
Profiles are also used by certain on-line retailers such as Amazon, which gathers information about a user's browsing habits and purchase history in order to suggest products which the user may wish to purchase. The user profile contains a mixture of information to which marketing intelligence can be applied, such as social demographic information based on the user's age, gender etc, together with dynamic information such as purchasing history and habits which are updated as required and can help filter searches or provide recommendations for a user to purchase.
In recent times the concept of a semantic web has gained prominence, whereby information and documents on the World Wide Web are marked up with a computer processable machine readable meaning, which can then be interpreted by related processing pools. One of the key benefits of the semantic web is to enable better machine searching of webpages, as the relevance of content is understood by a machine ultimately in the same way that a human user would understand it.
Semantic technologies have been proposed for a number of applications, see for example the document “Semantic User Profiles and their Applications in a Mobile Environment” by Andreas von Hessling, Thomas Kleemann and Alex Sinner of the Koblenz University in Germany (published by IASON-Project at University of Koblenz-Landau, Institut für Informatik, Arbeitsgruppe Künstliche Intelligenz, Universitätsstr. 1 56070 Koblenz which proposes a peer-to-peer based mobile environment consisting of stations providing semantic services and users with mobile devices which manage their owner's semantic profile. The key motivation here is to enable users of mobile devices to easily find goods and services which are both close to their location and which are of interest to them in accordance with their profile.
Another semantic profile is disclosed in the document “An Empirical Investigation of Learning from the Semantic Web” by Peter Edwards, Gunnar Aastrand Grimnes and Akin Preece of Aberdeen University, Proceedings of ECMUPKDD-2002 Semantic Web Mining Workshop, which explores the impact of the semantic web on machine learning algorithms used for user profiling and personalization, again with the motivation for enabling searching to be performed accurately and providing relevant results.
In the fields of computer games and of learning applications, users also have user profiles. Typically these are simple profiles comprising for example the user's name and virtual character appearance, together with information on the score that a character has accumulated during the game or past performance in a particular learning application. At present, a player's achievements in a learning application can only be stored and only have a meaning within that particular learning application. Also in the games environment a player's achievements in one game are not transferable to upgrade their status or enhance their progress in other games, except for a limited number of circumstances where the games are part of the same series written by the same company.
Approaches for reusable software components persistent between contexts are described in WO 02/10911, WO 99/17232, WO 02/48920 and WO 03/044664, which are of interest for general background information. However, there is currently no way for a user profile to be ported between different games or learning applications so that user profiles including learning objectives can be seamlessly used across a range of learning applications.